Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add swan-gtm/gtm-skills --skill battlecardgit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/swan-gtm/gtm-skills/battlecard)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/battlecard"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/battlecard/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/battlecard"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/battlecard.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.01046 |
| Opus 5 | $0.00018 | $0.00523 |
| Sonnet 5 | $0.00007 | $0.00209 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
battlecard scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructions
State check. This skill assumes the org has at least basic positioning established — a value prop and competition notes saved to org knowledge. Load org competition knowledge via swan-get-memory. If there's no competition slot at all and no value prop on file, the battlecard will end up generic — build out positioning first (the positioning capability has its own setup flow) before generating a battlecard. If competition notes exist for some competitors but not this one, that's fine: this run becomes the create-from-scratch path.
When to use
- "Build me a battlecard for X."
- "Update our notes on competitor X."
- "How do we beat X?"
- Before a competitive deal cycle.
What it produces
A six-section battlecard in canonical format, saved to org knowledge via swan-update-knowledge-competition and returned to the user.
Step 1 — Identify the competitor
Get the competitor name and primary URL from the user. Confirm the spelling. If the user has prior notes, load them first via swan-update-knowledge-competition (the read path is via swan-get-memory on the competition slot).
Step 2 — Pull existing intelligence
swan-get-memory for org competition knowledge. If there's already a section on this competitor, treat the task as update, not create from scratch. Preserve what's true; only revise what's stale.
Step 3 — Gather fresh signals
Make a small number of cheap queries. Do not dump everything; pick the highest-signal sources first.
swan-fetch-scraped-urlon the competitor's homepage and pricing page. Capture: the headline value prop, the primary CTA, the named target customer/segment.swan-fetch-scraped-urlon their /customers or case-study page if linked. Capture 3-5 named customers and any pattern.swan-linkedin-social-media-presenceon the competitor's company LinkedIn. Capture: posting cadence, hiring areas (titles in recent posts), leadership announcements.swan-website-trafficon the competitor's domain. Capture: traffic order of magnitude (10k, 100k, 1M monthly) and trend direction.swan-fetch-business-eventsfor funding/leadership/partnership events.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 93 lines · 36 tokens per session scan A 31ecda013325
battlecard is a skill published in the GitHub repository swan-gtm/gtm-skills (153 stars, last pushed 2d ago), licensed MIT. It adds 36 tokens to every session and 1,046 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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